Having invented Diffusion Tensor Magnetic Resonance Imaging (DT-MRI), we are continuing to use it as a means to probe tissue structure and to assess and diagnose neurological and developmental disorders. DT-MRI measures a diffusion tensor of water on a pixel-by-pixel basis within tissue, noninvasively and in vivo. It consists of (a) relating an effective diffusion tensor to the measured MR spin echo signal, (b) estimating an effective diffusion tensor, D, in each pixel from a set of diffusion-weighted MR images, and, (c) calculating and displaying information derived from D, including local fiber-tract orientation, the mean-squared distance water molecules diffuse in any given direction, the orientationally-averaged mean diffusivity, and other scalar invariant quantities that are independent of the laboratory coordinate system. These scalar parameters behave like quantitative histological or physiological stains, yet they are "developed" without requiring exogenous contrast agents or dyes. For example, the orientationally-averaged diffusivity (or Trace) has been the most successful imaging parameter proposed to date to visualize an acute stroke in progress. Moreover, we have shown that DT-MRI is effective in identifying Wallerian degeneration often associated with chronic stroke. Preliminary studies in kittens have also shown DT-MRI to be useful in following early developmental changes occurring in cortical gray and white matter, which are not possible to detect using other means.One recent innovation due to Carlo Pierpaoli and Sinisa Pajevic has been to use color to represent white matter fiber tract orientation in the brain. This has allowed us to identify and differentiate anatomical white matter pathways that have similar structure and composition, but different spatial orientations. Color maps of the human brain clearly show the main association, projection, and commissural white matter pathways. It has also allowed us to perform detailed studies of the brain's structural anatomy, which previously could only be performed using laborious, invasive histological methods. Recently, in order to assess anatomical connectivity between different functional regions in the brain, we proposed and demonstrated a way to use DT-MRI data to trace out nerve fiber tract trajectories there. This work was made possible by contributions by Sinisa Pajevic and Akram Aldroubi who developed a general mathematical framework for obtaining a continuous, smooth approximation to the measured discrete, noisy, diffusion tensor field data. We have also derived the form of the parametric distribution governing the statistical variability of diffusion tensor data, and developed non-parametric (bootstrap) methods for determining features of this statistical distribution from experimental DT-MRI data. These developments have allowed us to apply powerful hypothesis tests to address a wide variety of important biological and clinical questions that previously could only be tackled using ad hoc methods. Finally we are addressing other key methodological issues that will allow us to perform quantitative longitudinal or multi-center DT-MRI studies. Collectively, these developments have significantly advanced the utility and scope of clinical applications of DT-MRI.